Step Function Grid using Python

 import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

X, Y = np.meshgrid(x, y)

Z = np.heaviside(np.sin(X) * np.cos(Y), 0.5)

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(X, Y, Z, cmap='viridis')

ax.set_title("Step Function Grid")

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Importing Required Libraries

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

matplotlib.pyplot is used for plotting.

mpl_toolkits.mplot3d.Axes3D enables 3D plotting support.

numpy is used for numerical operations, especially arrays and math functions.

2. Creating the Grid

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

X, Y = np.meshgrid(x, y)

np.linspace(-5, 5, 100) creates 100 points between -5 and 5 for both x and y.

np.meshgrid(x, y) creates 2D coordinate matrices from the 1D x and y arrays. X and Y are 2D arrays representing all combinations of x and y.

3. Defining the Step Function Surface

Z = np.heaviside(np.sin(X) * np.cos(Y), 0.5)

This defines the Z values (heights) of the surface.

np.sin(X) * np.cos(Y) creates a pattern of values based on sine and cosine waves.

np.heaviside(..., 0.5) converts those values into step-like (binary) outputs:

Returns 1 where the argument is positive,

0 where it's negative,

0.5 exactly at zero (by definition here).

This creates a checkerboard-like grid of 0s and 1s.

 4. Creating the 3D Figure and Axes

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

plt.figure() initializes a new figure.

add_subplot(111, projection='3d') adds one 3D subplot to the figure.

 5. Plotting the Surface

ax.plot_surface(X, Y, Z, cmap='viridis')

plot_surface() creates a 3D surface plot.

X, Y, Z define the surface coordinates.

cmap='viridis' sets the color gradient for the surface.

 6. Adding Title and Displaying the Plot

ax.set_title("Step Function Grid")

plt.show()

set_title() adds a title to the plot.

plt.show() renders and displays the plot window.